A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
CGAN fails to improve deterministic sequence predictions, revealing a theoretical limitation.
Unified analysis for deterministic samplers in diffusion models.
Improves efficiency of simulators that fail to return.
Generative models using PDMPs with explicit jump rates and kernels.
In this paper, we analyse piecewise deterministic Markov processes, as introduced in Davis (1984). Many models in insurance mathematics can be formulated in terms of the general concept of piecewise deterministic Markov processes. In this context, one is interested in computing certain quantities of interest such as th…
We consider an individual or household endowed with an initial capital and an income, modeled as a deterministic process with a continuous drift rate. At first, we model the discounting rate as the price of a zero-coupon bond at zero under the assumption of a short rate evolving as an Ornstein-Uhlenbeck process. Then, …
Develops a deterministic method to approximate NSDEs for better uncertainty quantification.
State-space systems generate probabilistic dependencies between inputs and outputs.
New PDMP samplers improve BNN inference with accelerated computation.
We study online prediction of bounded stationary ergodic processes. To do so, we consider the setting of prediction of individual sequences and build a deterministic regression tree that performs asymptotically as well as the best L-Lipschitz constant predictors. Then, we show why the obtained regret bound entails the …
Modeling maximum drawdown records in capital markets using PDMP.
We consider deterministic Markov decision processes (MDPs) and apply max-plus algebra tools to approximate the value iteration algorithm by a smaller-dimensional iteration based on a representation on dictionaries of value functions. The setup naturally leads to novel theoretical results which are simply formulated due…
A model of fluctuations in the market price including many deterministic dealers, who predict their buying and selling prices from the latest price change, is developed. We show that price changes of the model is approximated by ARCH(1) process. We conclude that predictions of dealers affected by the past price changes…
We consider an economic agent (a household or an insurance company) modelling its surplus process by a deterministic process or by a Brownian motion with drift. The goal is to maximise the expected discounted spendings/dividend payments, given that the discounting factor is given by an exponential CIR process. In the d…
Study on regret minimization in deterministic MDPs.
Paper bounds PAC RL sample complexity in deterministic MDPs.
Stochastic encoders outperform deterministic ones in 'perfect perceptual quality'.
Develops new bounds for deterministic samplers in diffusion models.
New method approximates diffusion process posteriors using moment functions.
Paper presents a deterministic method for diverse subset selection.
This paper analyses the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al., 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, (Srinivas et al., 2010) pro…
This paper analyzes the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al, 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, Srinivas et al proved that t…
The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for reconstructing an input. The encoder achieves this by sampling from a distribution for every input, instead of outputting a deterministic c…
Study compares deterministic and probabilistic ML for precise AM component dimensions.
The paper sets criteria for no arbitrage in complex financial models.
New algorithms reduce regret in both stochastic and deterministic environments.
Framework simulates market microstructure with stable Hawkes processes.
Identifies most probable flows for Kunita SDEs in fluid dynamics.
The log returns of financial time series are usually modeled by means of the stationary GARCH(1,1) stochastic process or its generalizations which can not properly describe the nonstationary deterministic components of the original series. We analyze the influence of deterministic trends on the GARCH(1,1) parameters us…
SLEIPNIR improves Gaussian process regression with derivatives, scaling up efficiently and accurately.
KL annealing helps VAEs avoid posterior collapse and overfitting.
Recently there have been exciting developments in Monte Carlo methods, with the development of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-time, rather than discrete-time, Markov processes. This has led to some fundamentally new Monte Carlo algorithms which can be used to sample f…
New model predicts dynamic tax evasion with audits and imitation.
A new algorithm optimizes Gaussian process posterior mean functions efficiently.
We propose an artificial market model based on deterministic agents. The agents modify their ask/bid price depending on past price changes. The temporal development of market price fluctuations is calculated numerically. A probability density function of market price changes has power law tails. Autocorrelation coeffic…
Machine learning infers time-reversible dynamics from data.
We introduce a prototype model in an attempt to capture some aspects of market dynamics simulating a trading mechanism. The model description starts with a discrete-space, continuous-time Markov process describing arrival and movement of orders with different prices. We then perform a re-scaling procedure leading to a …
This study models target trajectories using stochastic processes for efficient tracking.
New method distinguishes stochastic from deterministic signals using excursion counts.
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the attributions of hidden predictors or the nonlinear mechanism of an estimator, while the bright parameters characterize how h…
New method uses PDMPs with sub-sampling for efficient sampling from posterior distributions.
Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scr…
In this paper, we investigate Parisian ruin for a Lévy surplus process with an adaptive premium rate, namely a refracted Lévy process. More general Parisian boundary-crossing problems with a deterministic implementation delay are also considered. Our main contribution is a generalization of the result in Loeffen et al.…
WSD uses a deterministic model to accelerate diffusion-based sampling.
Algorithm balances learning and coverage for multi-robots over unknown fields.
The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in . This distance plays a fundamental role in machine learning and signal processing: It is a member of the broader class of -divergences, and it is related to …
In this paper, we formulate a general time-inconsistent stochastic linear--quadratic (LQ) control problem. The time-inconsistency arises from the presence of a quadratic term of the expected state as well as a state-dependent term in the objective functional. We define an equilibrium, instead of optimal, solution withi…
Infinitesimal boosting converges to a deterministic process in large sample limit.